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Related Concept Videos

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Development and validation of natural language processing algorithms in the national ENACT network.

Yanshan Wang1,2,3, Jordan Hilsman1,2, Chenyu Li1,2,3

  • 1Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.

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|September 22, 2025
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Summary

The ENACT NLP Working Group successfully deployed natural language processing infrastructure across 13 sites, enabling access to clinical narratives for translational research. This demonstrates the feasibility of federated NLP deployment and highlights the importance of addressing data heterogeneity.

Keywords:
ENACTTranslational researchelectronic health recordsnatural language processingnetwork

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Translational Research

Background:

  • Electronic Health Record (EHR) data are crucial for advancing translational research and AI.
  • Clinical narratives within EHRs contain valuable information requiring Natural Language Processing (NLP) for research.
  • The ENACT network aims to provide access to structured EHR data across 57 Clinical and Translational Science Awards (CTSA) hubs.

Purpose of the Study:

  • To establish and operationalize the ENACT NLP Working Group for making NLP-derived clinical information accessible and queryable across the ENACT network.
  • To develop and validate NLP algorithms for diverse clinical tasks within specific disease contexts.
  • To extend the ENACT ontology and common data model to incorporate NLP-derived data while ensuring compatibility with existing research networks like SHRINE.

Main Methods:

  • Established the ENACT NLP Working Group with 13 sites selected based on access to clinical notes, IT infrastructure, NLP expertise, and institutional support.
  • Organized sites into five focus groups targeting specific clinical tasks, with each group comprising development and validation sites.
  • Extended the ENACT ontology, standardized NLP-derived data, and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

Main Results:

  • Achieved 100% site retention and successfully deployed NLP infrastructure across all participating sites.
  • Developed and validated NLP algorithms for phenotyping rare diseases, social determinants of health, opioid use disorder, sleep, and delirium.
  • Observed performance variability (F1 scores 0.53-0.96) across sites, underscoring the impact of data heterogeneity on NLP model generalizability.

Conclusions:

  • Demonstrated the feasibility of deploying NLP infrastructure across large, federated research networks.
  • The focus group approach was found to be more practical than general-purpose NLP strategies.
  • Key challenges identified include data heterogeneity and the need for robust collaborative governance, providing a foundation for other networks to implement NLP for translational research.